Instructions to use MRNH/llama-2-13b-GEC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MRNH/llama-2-13b-GEC with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("daryl149/llama-2-13b-chat-hf") model = PeftModel.from_pretrained(base_model, "MRNH/llama-2-13b-GEC") - Notebooks
- Google Colab
- Kaggle
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Download README.md from MRNH/llama-2-13b-GEC: direct link, hf CLI and curl.
- Browser
- Download file 597 Bytes
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https://huggingface.co/MRNH/llama-2-13b-GEC/resolve/main/README.md
- Command line
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hf download hf://MRNH/llama-2-13b-GEC/README.md
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curl -L -o README.md https://huggingface.co/MRNH/llama-2-13b-GEC/resolve/main/README.md
597 Bytes
metadata
library_name: peft
Load the Lora model
''' import torch from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = "lucas0/empath-llama-7b" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained(cwd+"/tokenizer.model")
model = PeftModel.from_pretrained(model, peft_model_id) '''
Training procedure
Framework versions
- PEFT 0.5.0